alexander-rakhlin/DeepCycle

code for DeepCycle paper

DeepCycle

This README outlines steps required to reproduce approach from DeepCycle manuscript.

Requirements

keras, UMAP, cv2, albumentations, classification_models
Install customized version of SOMPY

How ro run

1. Download data from EBI BioStudies repository and deep learning models from our EMBL hosting

2. Unzip to data/Timelapse_2019 folder preserving directory structure. You will have:

   root|
       |-data|
       |     |-Timelapse_2019|
       |                     |-BF/
       |                     |-Cy3/
       |                     |-DAPI/
       |                     |-GFP/
       |                     |-curated_tracks.csv
       |                     |- ...
       |-src/
       |...
   

3. cd src

4. Prepare the data:
   python data_prepare.py

   • Cleans and removes unnecessary columns. Stores as statistics_clean.csv in data/Timelapse_2019 dir
   • Aligns ~1000 curated tracks based on division events, calculates mean intensities track/frame wise. Stores as intensities.csv
   • Calculates intensity statistics and adds virtual class 1-4 to each tracked cell. Resulting data to be stored in statistics_mean_std.csv

5. Tran the model:
   python model_train.py
   Trains the model on curated tracks (less double division tracks) using double division tracks as validation set. Saves best models in checkpoints dir

6. Generate cell descriptors with checkpoint.r34.sz48.03-0.73.hdf5 as default model:

   • from validation set (double division tracks) only:
     python encode.py --mode encode_val
   • from all available tracks:
     python encode.py --mode encode_all
     Descriptors are saved in descriptors.r34.sz48.pkl and descriptors_all.r34.sz48.pkl in data/Timelapse_2019 dir.

7. Generate embeddings for all dataset. Compute intense, consider using supplied embeddings_preds_all_batch<i>.npz instead:
   python all_cells_prediction.py

8. cd ..

9. start jupyter notebook and open timelapse_projection2019.ipynb